0410431 20240103182213.620060210235959.9 9 s. cav_un_epca*02573890167-8655211165-1173Elsevier cav_un_auth*0101174 Paclík Pavel UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101171 Novovičová Jana UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 12B 09K VS96063 MŠMT CZ cav_un_auth*0025066 IAA2075608 GA AV CZ cav_un_auth*0012931 IAA2075606 GA AV CZ cav_un_auth*0012930 AV0Z1075907 The Laplace kernel rule for the road sign classification based on a priori information about road signs grouping has been developed. The smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method using the Expectation-Maximization algorithm. The new classification algorithm has been successfully tested on more than 1100 images of 43 road sign types. The comparison with the Bayes classifier assuming the Gaussian mixtures has been made. BB RO http://hdl.handle.net/11104/0130520 UTIA-B 20000147 2000 cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 21 13/14 2000 1165 1173 Elsevier